Weighted multimodal biometric recognition algorithm based on histogram of contourlet oriented gradient feature description

• Autorzy: Zhang, Xinman , Cheng, Dongxu , Xu, Xuebin
• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

Although the unimodal biometric recognition (such as face and palmprint) has higher convenience, its security is also relatively weak. The recognition accuracy is easy affected by many factors such as ambient light and recognition distance etc. To address this issue, we present a weighted multimodal biometric recognition algorithm with face and palmprint based on histogram of contourlet oriented gradient (HCOG) feature description. We employ the nonsubsampled contour transform (NSCT) to decompose the face and palmprint images, and the HOG method is adopted to extract the feature, which is named as HCOG feature. Then the dimension reduction process is applied on the HCOG feature and a novel weight value computation method is proposed to accomplish the multimodal biometric fusion recognition. Extensive experiments illustrate that our proposed weighted fusion recognition can achieve excellent recognition accuracy rates and outmatches the unimodal biometric recognition methods.

Słowa kluczowe

PL

biometryczna kontrola dostępu; rozpoznawanie twarzy; ekstrakcja cech; fuzja obrazów; rozpoznawanie obrazu; rozpoznawanie odcisków palców

EN

biometric access control; face recognition; feature extraction; image fusion; image recognition; palmprint recognition; transform

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 381–-384
• Opis fizyczny: Bibliogr. 15 poz., il., wz.

Twórcy

autor

Zhang, Xinman

• School of Electronics and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University, No.28 xianning west road, Xi’an, P.R.China,

autor

Cheng, Dongxu

• School of Electronics and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University, No.28 xianning west road, Xi’an, P.R.China,

autor

Xu, Xuebin

• Guangdong Xi'an Jiaotong University Academy, No. 3, Daliangdesheng East Road, Foshan, China,

Bibliografia

• 1. A. K. Jain, A. Ross, and S. Prabhakar. “An Introduction to Biometric Recognition,” IEEE Trans. Circ. Syst. Vid. Tech., 2004, vol. 14 pp. 4-20, http://dx.doi.org/10.1109/TCSVT.2003.818349.
• 2. J. Chen, V. Patel, and L. Liu, et al. “Robust Local Features for Remote Face Recognition,” Image and Vision Computing, 2017, vol. 64, pp. 34-46, http://dx.doi.org/10.1016/j.imavis.2017.05.006.
• 3. A. Ghasemzadeh, H. Demirel. “3D discrete wavelet transform-based feature extraction for hyperspectral face recognition,” IET Biometrics, 2018, vol. 7, pp. 49-55, http://dx.doi.org/10.1049/iet-bmt.2017.0082.
• 4. X. Xie, J. Lai, and W. Zheng. “Extraction of illumination invariant facial features from a single image using nonsubsampled contourlet transform,” Pattern Recogn., 2010, vol. 43, pp. 4177-4189, http://dx.doi.org/10.1016/j.patcog.2010.06.019.
• 5. N. Dalal, B. Triggs. “Histograms of oriented gradients for human detection,” International Conference on computer vision & Pattern Recognition, 2005, pp. 886-893, http://dx.doi.org/10.1109/CVPR.2005.177.
• 6. C. Y. Low, A. B. Teoh, and C. J. Ng. “Multi-Fold Gabor, PCA and ICA Filter Convolution Descriptor for Face Recognition,” IEEE Trans. Circ. Syst. Vid. Tech., 2019, vol.29, pp.115-128, doi: 10.1109/TCSVT.2017.2761829.
• 7. A. Younesi, M.C. Amirani. “Gabor filter and texture based features for palmprint recognition,” Procedia Comput. Sci. 2017, vol. 108, pp. 2488–2495, http://dx.doi.org/10.1016/j.procs.2017.05.157.
• 8. S. W. Zhang, H. X. Wang, and W. Z. Huang, et al. “Combining modified LBP and weighted SRC for palmprint recognition,” Signal Image Video Process. 2018, vol. 12, pp. 1035–1042, http://dx.doi.org/10.1007/s11760-018-1246-4.
• 9. M. Haghighat, M. Abdel-Mottaleb, and W. Alhalabi. “Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition,” IEEE Trans. Inf. Foren. Sec., 2016, vol. 11, pp. 1984-1996, http://dx.doi.org/10.1109/TIFS.2016.2569061.
• 10. N. Saini, A. Sinha. “Face and palmprint multimodal biometric systems using Gabor-Wigner transform as feature extraction,” Pattern Anal. Appl., 2015, vol. 18, pp. 921-932, http://dx.doi.org/10.1007/s10044-014-0414-6.
• 11. W. F. Li, Y.C. Wang, and Z. Xu Z, et al. “Weighted contourlet binary patterns and image-based fisher linear discriminant for face recognition,” Neurocomputing, 2017, vol. 267, 436-446, http://dx.doi.org/10.1016/j.neucom.2017.06.045.
• 12. M. N. Do, M.vetterli. “The Contourlet Transform: An Efficient Directional Multiresolution Image Representation,” IEEE Transa. Image Process., 2006, vol. 14, pp.2091-2106, http://dx.doi.org/10.1109/TIP.2005.859376.
• 13. A. L. D. Cunha, J. P. Zhou, and M. N. Do. “The Nonsubsampled Contourlet Transform: Theory, Design, and Applications,” IEEE Transa. Image Process., 2006, 15, pp. 3089-3101, http://dx.doi.org/10.1109/TIP.2006.877507.
• 14. A. Bosch, A. Zisserman, and X. Munoz, “Representing shape with a spatial pyramid kernel,” International Conference on Image and Video Retrieval, 2007, pp. 401-408, http://dx.doi.org/10.1145/1282280.1282340.
• 15. D. Zhang, W. K. Kong, and J. You, et al. “Online palmprint identification,” IEEE Trans. Pattern Anal. 2003, vol. 25, pp. 1041–1050, http://dx.doi.org/10.1109/TPAMI.2003.1227981.

Uwagi

1. This work is supported by National Natural Science Foundation (No. 61673316), Major Science and Technology Project of Guangdong Province (No. 2015B010104002).

2. Track 2: Computer Science & Systems

3. Technical Session: 12th International Symposium on Multimedia Applications and Processing

4. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).

Identyfikatory

DOI

10.15439/2019F178